{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDataSet(fileName):\n",
    "    numFeat = len(open(fileName).readline().split('\\t')) - 1  # 特征数量\n",
    "    dataMat = []\n",
    "    labelMat = []\n",
    "    fr = open(fileName)\n",
    "    for line in fr.readlines():\n",
    "        lineArr = []\n",
    "        curLine = line.strip().split('\\t')\n",
    "        for i in range(numFeat):\n",
    "            lineArr.append(float(curLine[i]))\n",
    "        dataMat.append(lineArr)\n",
    "        labelMat.append(float(curLine[-1]))\n",
    "    return dataMat, labelMat\n",
    "\n",
    "# 计算最佳拟合直线\n",
    "def standRegres(xArr, yArr):\n",
    "    xMat = mat(xArr)\n",
    "    yMat = mat(yArr).T\n",
    "    xTx = xMat.T * xMat  # 矩阵乘法\n",
    "    if linalg.det(xTx) == 0.0:  # 判断行列式是否为0\n",
    "        print('This matrix is singular, cannot do inverse')\n",
    "        return\n",
    "    ws = xTx.I * (xMat.T * yMat)  # mat.I表示逆运算\n",
    "    return ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "xArr, yArr = loadDataSet('ex0.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.0, 0.067732], [1.0, 0.42781]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xArr[0: 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[3.00774324],\n",
       "        [1.69532264]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws = standRegres(xArr, yArr)\n",
    "ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xMat = mat(xArr)\n",
    "yMat = mat(yArr)\n",
    "yHat = xMat * ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ab0yeqakNUfXHtWnr45NH/KrK1367ij9v3AlArx4+/v6DcyhxyNv/cMZIgPgI3SfCl844Pn4+Wao0ko3iO54FemPyzPwlm9Pm0aHlhOf6HXuYfs9L8eNfXDWW80YOSPn6H84Y6RrYk7mlkQSsjLITWKA3Js94yc3HJjxVlSsWruC1rbsBOPbInvztpkn0KM5kdZT03Db6tuqazpHd/5rGmC6XLnjGyh4H9QswdPbieJB/4NrTee27Z2c9yAOOi6JZdU3nsRG9MXmmasowqv64tlX6xu8T5l8yiumjBnL+z19i47sfA3BCaW+WfOtMin0dN+6zjb67lgV6Y/JMLHjOeWoD9dE9Wvv18nPr9BEcc0RPTvju4njbP3ztDCaccEyn9csCe9ewQG9Mnurd8/B+rt8+59Pc+fxmtu+O5MlHHx9k0fUTKPKw1LDJfRbojckzTlvxfeePa+PXF319AmPL+3VV90wXsEBvTJ5xq1nvWVzEptunetqc2+QXC/TG5IHEfVjdKugPNTZbkC9QFuiNyXFOG387sZr1wmV19MbkOC+rVFrNemGzEb0xOS7VSpICVrNuLNAbk8seeHmr6zW31SlN4bFAb0wO+mjfIcbc/nz82FckNCU8CWupGpPIcvTG5Jh7lv1fiyC/YvYXufPSUZQFA6228DMGbERvTM7YufcA4370l/jxv086ke9MjozabXkBk4qN6I3JAdXPbmoR5AEW/T3kaScpY2xEb0w3FqoPM7F6Wfy4uEhojObiQ/VhZi9aD2CjeZOSjeiN6aZ+UPNGiyA/4MiSeJCPCTc0MX/J5s7umskxngO9iPhEpFZEnna4JiLyMxHZIiLrRGRswrWpIrI5em1WtjpuTLbV1IaYWL2MobOeYWL1si5Li2z7YB9DZj3D71b8E4DbLxzBtuppvPfxAcf2qerojYHMUjc3ABuBIx2unQucFP1zBvBL4AwR8QH3AucAO4CVIvKUqv6jXb02JsucVnzsirTIjY+s4YmEL5g35k7hiJ6R/02Dvfx8tL/B8XU1tSFL3xhXnkb0IjIImAbc59LkQuC3GrECCIrIAGAcsEVV31bVQ8DD0bbGdCtOywh0Zlrkzff3MmTWM/Egf8elo9hWPS0e5AE0xX7flr4xqXgd0d8N3AT0cbleBmxPON4RPed0/gynNxCRmcBMgPLyco/dMiY73DbUTjyfuEJktpYVUFW+9tvV/Hnj+wD06uHj7z84h5Kk/VUB9oSdR/Op+m8MeAj0InI+sFNVV4vIWW7NHM5pivOtT6ouBBYCVFRUpBi7GJN9A4MBx1x3bMXHjkjtrN+xh+n3vBQ/vvfKsUw7dUCLNjW1Ieb+aYNryia5n8Y48TKinwhcICLnASXAkSLye1W9OqHNDuD4hONBQB3Qw+W8Md1K5fBSHlzxTotRSOIyAulSO5mM9FWVKxau4LWtuwHo36cnL908iR7FLTOpNbUhqh5bS0NT+nGPLXdgUhFNlfhLbhwZ0f+nqp6fdH4a8E3gPCKpmZ+p6jgRKQbeBL4IhICVwJWquiHV51RUVOiqVasyuQ9j2sxpPXcBJpxwFNs+DKfczAMiXwiJrw34fa5LEKzatptLFrwaP37g2tOpHN7f8X0nVi/zXFGzrXqap3Ymf4nIalWtcLrW5gemROQ6AFVdACwmEuS3APuBL0evNYrIN4ElgA+4P12QN6azOY3WFXjlrd0pAzyAT8R1pJ8Y6JualQvueYkNdR8D8KnS3iz91pkU+9zrIbzm3cssbWPSyCjQq+pyYHn05wUJ5xX4hstrFhP5IjCmW3ILqOmCfPJI3u09X/q/D7j6f1+LH//ha2cw4YRj0vYrVTllYh8sbWPSsSUQTMFzm4h1E9vMwymvn/ieDU3NTLpzOdt3R9571PFBnrh+AkVF3vZtTZdVLbMNRYxHFuhNwauaMszTnqvQcjOPidXLHIO8AOeechwnfe/Z+LnHr5/AaYP7Ac5lmhBJIYXqw/hEaEoT5S0nbzJhgd4UpORge/FpZbywaRd19WH6Bvx8fKCBZodYu/9QY/wp1FQpn/teiuz89PmTjuG3/28cIhL/3OQyzarH1oJCQ/QD0wV5y8mbTNmiZqbgxIJtKFpNE6oP8+CKd6gcXsrW6mmI4BjkAT7a38DsReupqQ15ql1/e9c+nlxzuKLYaeK3oUnjQT4dy8mbtrBAbwqOW5XNgyveoaY2lHYCNFZVUzVlGAGHJ1gTM/CxB6tiC6S19QlW2znKtIelbkzBSZVy8bpmTF19mBljyljx9oc8vHJ7i2vJY/PEcstMJ37BNvk27WcjelNwUqVc6urDBAP+tO9xXN8Shsx6plWQT/W+gONvAX6f4HepxLFUjckGC/SmICSuNb/vYKNru4HBAHMuGOEaeAH8RcK7e5zXhk/1vhBZF2feRSPp1+vwl0nvHsVcPu74+CSrLzpxGwz4KfEXceMja7p0fXyT+yx1Y/JecqVLfbiBIqA5qV1s9BzLgceqcvoG/IgQz93HJk6/PHEISze87ykVU7//EKPnLmVPuIG+AT/7Dh3+sqkPN/D46lCL/Ht3WR/f5IeM1rrpLLbWjcmWmtoQ33l0rWPJYjDgp3fPYk+Lkd37wpYW+ftXZ09iQN8AQ2c9k/YJWq+Sa/SdvkAsX2/cdMhaN8Z0d7FRsVtden24gT3hhpRBfufeA4z70V/ix/8+6US+M/lwzrwtk6tuEieJvayPb4xXlqM3ecupjDJZrI4+sQQy5ifPbWoR5Fd+7+wWQR6cJ1e9LXDQWuIksduEsa07b9rCAr3JC04be2cy+k1cW76uPsyQWc/wy+VvAXDz1OFsq55GaZ+erV4Xm1wtCwbite53XT4646dXk6trnL5ArALHtJXl6E3Oc1pPPuD3UeIvSvvwUyIBrvnsYH776j/j59beMpm+vdKXWzr16cZH1rjm7/1FwhElxdTvd08ddcTWhSZ/pcrRW6A3Oc9t4jIY8HOwsbnVF0DP4iLqU+y/CnD7hSO45rND2tWvIbOecb129+WjLWibrLLJWJPX3FI09eEGrh5fHl+sLHGlyFSrVb4xdwpH9Gz//xpuq1D6RCzIm05lgd7kvFSVL8n16Yl+vHgjO/cejB/fcekoLjltUNb65Vbtk251SmOyzSZjTc5zW1wMWk6yxqgqz6x/Nx7kA34fm26fmtUgD+7LCdsyw6azWaA3OS9W+eImMbXzRmgPQ2cv5vl/vA/AvVeOZePtUylx+aJoD6ucMd2FpW5MXpgxpiy+Q1MyBSbM+wuBHj7e2rUPgNI+PXn55kn0KO64sU7yUgpWOWO6StqqGxEpAV4EehL5YnhMVW9NalMFXBU9LAY+A5Sq6m4R2QbsBZqARrdZ4URWdWPawqnM0sn911YwafixndQrYzpHe6tuDgKTVPUTEfEDL4nIs6q6ItZAVecD86MfNh24UVV3J7xHpap+0PZbMCa1mtoQc/+0IW2QH9i3xIK8KThpA71GhvyfRA/90T+pfg34EvBQ+7tmjDc1tSGqHltLQ1P6apZMlxc2Jh94SlCKiE9E1gA7gedV9TWXdr2AqcDjCacVWCoiq0VkZorPmCkiq0Rk1a5du7zfgSl485ds9hTkwdaKMYXJU6BX1SZVHQ0MAsaJyCkuTacDLyelbSaq6ljgXOAbInKmy2csVNUKVa0oLS3N4BZMofO6eqRVvJhClVHJgarWA8uJjNqdXEFS2kZV66J/7wSeAMZl3EtjUkj1j9gnYhtrm4KXNkcvIqVAg6rWi0gAOBv4iUO7vsAXgKsTzvUGilR1b/TnycBt2eq8KWyxCdjknaIS3XnZKAvupuB5qboZAPxGRHxEBk+PqurTInIdgKouiLb7F2Cpqu5LeO2xwBMS2QOzGPiDqj6Xtd6bgvXoyu3c9Pi6lG369fJbkDcGb1U364AxDucXJB3/Gvh10rm3gVHt6qExSbwE+YDfx63TR3RSj4zp3uzJWJMz9h5oYOScpWnbldkTqMa0YIHe5IQHXt7K3D/9I2072zzbmNYs0JturX7/IUbf9nz8uHcPH/sOOT/9auWTxjizQG+6rXtf2NJqiWG3IA9Y+aQxLizQm25n196DnP6jP8ePi4uExubUT76WBQMW5I1xYYHeuOqKzal/+twmfrH8rfjxcUeW8N7HqdensZSNMalZoDctxIJ7qD6McHj1ulB9mNmL1gN0SLCvqw8zoXpZ/PjmqcO5/qwTGJpig20BW+PdGA8s0Ju45PXck5MlsW35sh1Ub33yDX7z6j/jx2tvmUzfXn4Agr38fLS/odVr+vXyU3vL5Kz2w5h8ZYHexM1fsjnteu51HhcQ82LbB/s4647l8eO5F4zg3yYMadHGbV8c21/bGO8s0Js4L0E8W8v8fvuRNSyqDcWP35g7hSN6tv7nuCfcejSf6rwxpjUL9CZuYDCQcsnfxEnPtk7Uvvn+Xibf9WL8eP4lp3JpxfEZ98nWlTfGOwv0BcopUFcOL+X3K95xbO8T4eLTIhtwf+uRNRlP1KoqM3+3muf/8T4AJf4ian8wmUAPX8p+Vg4v5cEV77SYL7AqG2MyY4G+ACVPuobqw1Q9tjblBpFNqjy+OtSmido3Qns4/+cvxY/vvXIs004d4Kmfj68OtfgsAS4+rcyqbIzJgAX6AuQ06ZpuKz6fSMYTtarKl/5nBSvePrzh2MC+Jbz69gf8ePFGx7RPYnmnEwVe2GRbTRqTCQv0BSjTypmA35c2yEPLvPl/Ld3Mz5Ztaf3Zew60SA8lpn2AFr9puMlm5Y8xhcACfQFKN+ma7OLTynhm3buO9eyJKoeX0tysnDn/BXZ85P39Y2mf2M/p2ESsMZnJaM9Ykx+qpgwj4E89CZromXXv8smBxrTtnl3/Hp/67uKMgnxMXX3Y00jdJmKNyZwF+gI0Y0wZF5/mfTLzo/0NNKRZVAzgw32H2tyngcGAp5G6rVBpTOYs0Beg79es50GXMsr2evz6CZRlmFqJjdIrh5ciKdrZHrDGtI0F+gJTUxtqVZeeSsDvIxjwp21XJHDXZaM4bXA/qqYMw1/kHLLLggEmnnAUvsiG8fH6fKBVKWUiv09sD1hj2sgmYwvM/CWbUwb5q8eX88KmXS1KHyF1NUwR0Kxwx9I3kWgAdxqaXz2+nIrBR1H1x7U0RReraVLlkde38/Tad13f3/aANaZ9RNOsDiUiJcCLQE8iXwyPqeqtSW3OAp4EtkZPLVLV26LXpgL/DfiA+1S1Ol2nKioqdNWqVZndifFk6KxnXAN9bL9Vp6dmAX7y3Cbe3XN4bfhTB/Xlzff2cqCxOX4u4PdR4i9yrNARIk/EhhuaW11zI8DW6mme2xtTqERktapWOF3zMqI/CExS1U9ExA+8JCLPquqKpHZ/U9Xzkz7YB9wLnAPsAFaKyFOqmn6XZ5MVyUE74C9iv0ugrZoyzPGp2dmL1jNj9MAWQf75G8/k2gdWtgjyECmPdBuZK2QU5MFKKY3JhrSBXiND/k+ih/7oH68p3nHAFlV9G0BEHgYuBCzQdwKnoJ2O01Oz4YYmHlq5HYBLThvEHZeOAjr+wSUrpTQmOzzl6KMj89XAicC9qvqaQ7PPishaoA74T1XdAJQB2xPa7ADOcPmMmcBMgPLycs83YJzV1Ib4zqOHc+FexEb+bv5adRaDj+4dP870wSuvbOcoY7LLU9WNqjap6mhgEDBORE5JavJ3YLCqjgJ+DtREzzuVXjhGHlVdqKoVqlpRWlrqrffGUWwkn0mQB+LpHSdlwUCLIA/OD155eRDL73MvoiwLBthaPY2XZ02yIG9MlmRUXqmq9cByYGrS+Y9V9ZPoz4sBv4gcQ2QEn7jY+CAiI37TgbzsFOVkYDDA6OODrc67pVBmjClj3kUjKQsGECJBet5FI+nXy70csywYYP4loxzbWKrGmI6RNnUjIqVAg6rWi0gAOBv4SVKb44D3VVVFZByRL5APgXrgJBEZCoSAK4Ars3wPJklbcuclxUWE6sPxVMwRPYvZd7AxbQplxhjnJYO//egakh+m9RdJ/L1mjClr8+YlxpjMeMnRDwB+E83TFwGPqurTInIdgKouAC4BrheRRiAMXBGdxG0UkW8CS4iUV94fzd2bDpRp7lygRfXMyh7IEagAAA1pSURBVO+dTWmfnu3qg0+E5qTU0eXjjm8RyN2+JIwx2eWl6mYdMMbh/IKEn+8B7nF5/WJgcTv6aDKUaqcoJ7FwfNPUYXz9rBPb/fnzl2x2XBvH1pE3pmvYk7F5KF1A9Ym0mqhdc8s5BHv1yMrnu6WObB15Y7qGBfo8lCqg9iwu4mBCmmbuBSP4twlDsvr5tqG3Md2LLWqWZ2pqQxSJc/miQIsg/8bcKVkP8uBedmkVNcZ0DRvR55F09fOxsz+95FQuqzjesU02xCZYraLGmO7BAn0eSVc/X+IvovYHkwn08L67VFtZRY0x3YcF+jxRUxtKWVJ5z5VjOP/UgZ3YI2NMd2E5+jwQS9m4Gdi3xIK8MQXMAn0emPunDa4pm4Dfx01Th3dyj4wx3YkF+hxXUxty3OQjxjbTNsZYoM9xtz/tvrR/WTBgQd4YY5OxuaqxqZlz7nqRD/cdcm1jdevGGLBAn5P+svF9vvKb1HvqBgN+G80bYwAL9DnlYGMTE+YtSzmKh8gE7JwLRnRSr4wx3Z0F+hzx9Lo6vvmH2rTtyuwpVGNMEgv03Vz4UBOjblvKoegaNVNGHMuSDe87thXg5VmTOrF3xphcYFU33dijq7bzmVueiwf55288k3NPGeC4ES/Y6pDGGGc2ou+G9h5oYOScpfHji8aW8V+XjQbg2gdWOu6uLliVjTHGmQX6bubXL29lzp8O18b/teosBh/dO37stp6NguXljTGOLNB3E/X7DzH6tufjx9dOGNKqcqamNuT6+n69/B3WN2NMbrNA3w38YvkWfvrc5vjxK7MmOebb5y/Z3OpcjMsS9MYYkz7Qi0gJ8CLQM9r+MVW9NanNVcDN0cNPgOtVdW302jZgL9AENKpqRdZ6n+N27T3I6T/6c/z4G5UnUDXl8AJkNbWhFpt3pFqGeE/Yfb0bY0xh8zKiPwhMUtVPRMQPvCQiz6rqioQ2W4EvqOpHInIusBA4I+F6pap+kL1u576fPreJXyx/K3688ntnU9qnZ/w4tvRwbFXKUH0YAceJWLCKG2OMu7SBXlWVyCgdwB/9o0ltXkk4XAEMylYHO1vyKDrbDx/V1YeZUL0sflw1ZRjfqDyxVTun3aLcgry/SKzixhjjylOOXkR8wGrgROBeVX0tRfOvAM8mHCuwVEQU+JWqLnT5jJnATIDy8nIv3co6p1F04oYe7f0CmPPUBn79yrb48ZpbziHYq4dj27oUaZpEwYCfOReMsIobY4wrT4FeVZuA0SISBJ4QkVNU9Y3kdiJSSSTQfy7h9ERVrROR/sDzIrJJVV90+IyFRFI+VFRUdMnUotMoOtzQxHcXrUMRxy8AtwCb+JtB/z49eX/vwfi1OdNP5tqJQ1P2JV1OHiLLHdiTsMaYdDKqulHVehFZDkwFWgR6ETkVuA84V1U/THhNXfTvnSLyBDCOyORut+M2it7f0NzqXLihiflLNrcI9LHgnpxPTwzy6+dMpk9J+lLIyuGlPLjiHdd0Tar+GmNMIi9VN6VAQzTIB4CzgZ8ktSkHFgHXqOqbCed7A0Wqujf682TgtmzeQDZ5GUUnSgy0yWkfpwBdFgw4BvnkeYHK4aU88vr2lEE+1l9jjEnHy4h+APCbaJ6+CHhUVZ8WkesAVHUBcAtwNPALEYHDZZTHEkn1xD7rD6r6XPZvo/1qakPsP9SY0WsSA61T2ieZ0wj8+zXrW4zcQ/Vhfr/inbSfHfD7bALWGOOJl6qbdcAYh/MLEn7+KvBVhzZvA6Pa2ccO45Zq8SI50Hr5TSD2xZD4uW1hSxEbYzJRsE/Gekm1JPOJ0Kzaqurmib/vSPva2BdD8udmypYiNsZkquACfVtH0wLcedmoVqPop9fVceOja1O+NnEEPrF6WZuDPFhe3hiTuYIJ9DW1IeY8tYH6Ni4V0DdpD9amZuWE7y5O+7q7Lx/d4nXtqZSxB6OMMW1REBuPxNIlbQ3y0HItmYdff6dFkHfbCAQOL0RWUxtiYvWyjOYBEgUDfuZf2vo3CmOMSacgRvReKmLSGRgMcKixmU9//9kW50uKizjQ2LrOPqauPtyuvLwAdyX9VmCMMZkoiBG9l3SJAFePL+fq8eWtRugBv4+KIf1aBPmff2kMZcFAyiAPkZTPnKc2tDnIXzW+3IK8MaZdCiLQe5nAVOCFTbv44YyR3HX5aMqCAQQY0LeEcEMTT66pi7d9+8fnMX3UQE9fIHsPNrYpZVQWDHDX5aP54YyRGb/WGGMSFUSgr5oyjIDfl7ZdLHDPGFPGy7Mm8R9fPIl39xyIX7//2gq2VU+jqCgy5vfyBdLUnHlWPlZCaSN5Y0w2FESOPhYw05VVxgJ38ubcAFvnnUf0Cd+4qinD2lUTn64fxhiTDQUR6OFwsHcLzLEHmh5dtZ2bHlsXP//wzPGM/9TRKd+zPU+5JrMSSmNMthVMoAf36hufCLdOP5lvPbImfm5seZBFX5+Y9j1njCmLPwjV3mBva8sbYzpCXgZ6t12i3CZPm1SZlbDByF+rzmLw0b0z+sz2pHECfh/zLhppAd4Y0yHyLtDX1Iao+uNaGqKToKH6MFV/jCxRkG4Z4n/77GDmXnhKmz43MY1TVx8m2MvPR/udq22CAT+9exZ32HaFxhiTSCJbwnYvFRUVumrVqja9dvTcpY7ljLG0iNuo+5VZk7I+CZq8BDHY6N0Y0zFEZHV0efhW8q680q1mvT7cwIwxZXz3vOEtzp/9mf5sq57WIZUuyTX5ZcGABXljTKfLu9RNKncs2cw9L2yJH7/+vS/Sv09Jh35mbLLWGGO6St4F+n4pcuOxIF81ZRjfqDyxM7tljDFdJu9SN7dOH4Hf576eZO0PzrEgb4wpKHk3oo+lSeYt3sj7ew/Gz8+ZfjLXThzaVd0yxpguk3cjeoDK4f1bBPn1cyZbkDfGFKy8G9ED9Cwu4txTjqNyWH8uO/34ru6OMcZ0qbQjehEpEZHXRWStiGwQkbkObUREfiYiW0RknYiMTbg2VUQ2R6/NyvYNOCnx+/jl1adZkDfGGLylbg4Ck1R1FDAamCoi45PanAucFP0zE/glgIj4gHuj108GviQiJ2ep78YYYzxIG+g14pPooT/6J/lx2guB30bbrgCCIjIAGAdsUdW3VfUQ8HC0rTHGmE7iaTJWRHwisgbYCTyvqq8lNSkDticc74ieczvv9BkzRWSViKzatWuX1/4bY4xJw1OgV9UmVR0NDALGiUjyyl9Oheua4rzTZyxU1QpVrSgtLfXSLWOMMR5kVF6pqvXAcmBq0qUdQOLM5yCgLsV5Y4wxncRL1U2piASjPweAs4FNSc2eAv41Wn0zHtijqu8CK4GTRGSoiPQAroi2NcYY00m81NEPAH4TraApAh5V1adF5DoAVV0ALAbOA7YA+4EvR681isg3gSWAD7hfVTdk/zaMMca4ybv16I0xphClWo++WwZ6EdkF/DPDlx0DfNAB3enO7J4Lg91zYWjvPQ9WVcdKlm4Z6NtCRFa5fZvlK7vnwmD3XBg68p7zclEzY4wxh1mgN8aYPJdPgX5hV3egC9g9Fwa758LQYfecNzl6Y4wxzvJpRG+MMcaBBXpjjMlzORfo021kkmoTlFzl4Z6vit7rOhF5RURGdUU/s8nrhjUicrqINInIJZ3Zv2zzcr8icpaIrIluAPTXzu5jtnn4d91XRP6UsOnRl7uin9kkIveLyE4RecPlesfEL1XNmT9EllF4C/gU0ANYC5yc1OY84FkiK2eOB17r6n53wj1PAPpFfz63EO45od0yIktwXNLV/e7g/8ZB4B9AefS4f1f3uxPu+bvAT6I/lwK7gR5d3fd23veZwFjgDZfrHRK/cm1E72UjE7dNUHJV2ntW1VdU9aPo4Qoiq4TmMq8b1vw78DiRfRJymZf7vRJYpKrvAKhqIdyzAn1ERIAjiAT6xs7tZnap6otE7sNNh8SvXAv0XjYy8bzZSY7I9H6+QmREkMvS3rOIlAH/AizoxH51FC//jT8N9BOR5SKyWkT+tdN61zG83PM9wGeILG2+HrhBVZs7p3tdpkPil5fVK7sTLxuZeN7sJEd4vh8RqSQS6D/XoT3qeF7u+W7gZlVtigz4cpqX+y0GTgO+CASAV0Vkhaq+2dGd6yBe7nkKsAaYBJwAPC8if1PVjzu6c12oQ+JXrgV6LxuZ5NtmJ57uR0ROBe4DzlXVDzupbx3Fyz1XAA9Hg/wxwHki0qiqNZ3Txazy+u/6A1XdB+wTkReBUUCuBnov9/xloFojyestIrIVGA683jld7BIdEr9yLXXjZSMTt01QclXaexaRcmARcE0Oj/ASpb1nVR2qqkNUdQjwGPD1HA3y4O3f9ZPA50WkWER6AWcAGzu5n9nk5Z7fIfIbDCJyLDAMeLtTe9n5OiR+5dSIXl02MvGyCUqu8njPtwBHA7+IjnAbNYdX/vN4z3nDy/2q6kYReQ5YBzQD96mqY4leLvD43/h24Ncisp5ISuNmVc3ppYtF5CHgLOAYEdkB3Ar4oWPjly2BYIwxeS7XUjfGGGMyZIHeGGPynAV6Y4zJcxbojTEmz1mgN8aYPGeB3hhj8pwFemOMyXP/HwgDeVxoi9HVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# flatten是numpy.ndarray.flatten的一个函数，即将numpy对象降为一维。\n",
    "# numpy对象.A（等效于numpy对象.getA()）变成了数组\n",
    "ax.scatter(xMat[:, 1].flatten().A[0], yMat.T[:, 0].flatten().A[0])\n",
    "\n",
    "xCopy = xMat.copy()\n",
    "yHat = xCopy * ws\n",
    "ax.plot(xCopy[:, 1], yHat)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.98647356],\n",
       "       [0.98647356, 1.        ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yHat = xMat * ws\n",
    "corrcoef(yHat.T, yMat)  # 计算两两组合的相关系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lwlr(testPoint, xArr, yArr, k=1.0):\n",
    "    xMat = mat(xArr)\n",
    "    yMat = mat(yArr).T\n",
    "    m = shape(xMat)[0]\n",
    "    weights = mat(eye((m)))  # 创建单位对角矩阵\n",
    "    for j in range(m):\n",
    "        diffMat = testPoint - xMat[j, :]\n",
    "        weights[j, j] = exp(diffMat * diffMat.T / (-2.0 * k**2))\n",
    "    xTx = xMat.T * (weights * xMat)\n",
    "    if linalg.det(xTx) == 0.0:\n",
    "        print('This matrix is singular, cannot do inverse')\n",
    "        return \n",
    "    ws = xTx.I * (xMat.T * (weights * yMat))\n",
    "    return testPoint * ws\n",
    "\n",
    "def lwlrTest(testArr, xArr, yArr, k=1.0):\n",
    "    m = shape(testArr)[0]\n",
    "    yHat = zeros(m)\n",
    "    for i in range(m):\n",
    "        yHat[i] = lwlr(testArr[i], xArr, yArr, k)\n",
    "    return yHat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.176513"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xArr, yArr = loadDataSet('ex0.txt')\n",
    "yArr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[3.20175729]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lwlr(xArr[0], xArr, yArr, 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "yHat = lwlrTest(xArr, xArr, yArr, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "xMat == mat(xArr)\n",
    "srtInd = xMat[:, 1].argsort(0)\n",
    "xSort = xMat[srtInd][:, 0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(xSort[:, 1], yHat[srtInd])\n",
    "\n",
    "ax.scatter(xMat[:, 1].flatten().A[0], mat(yArr).T.flatten().A[0], s=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实例：预测鲍鱼的年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reeError(yArr, yHatArr):\n",
    "    return ((yArr - yHatArr)**2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "abX, abY = loadDataSet('abalone.txt')\n",
    "yHat01 = lwlrTest(abX[0: 99], abX[0: 99], abY[0: 99], 0.1)\n",
    "yHat1 = lwlrTest(abX[0: 99], abX[0: 99], abY[0: 99], 1)\n",
    "yHat10 = lwlrTest(abX[0: 99], abX[0: 99], abY[0: 99], 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.825235689362394"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reeError(abY[0: 99], yHat01.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "429.8905618700943"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reeError(abY[0: 99], yHat1.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "549.1181708827294"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reeError(abY[0: 99], yHat10.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35709.281008018035"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yHat01 = lwlrTest(abX[100: 199], abX[0: 99], abY[0: 99], 0.1)\n",
    "reeError(abY[100: 199], yHat01.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "573.5261441893857"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yHat1 = lwlrTest(abX[100: 199], abX[0: 99], abY[0: 99], 1)\n",
    "reeError(abY[100: 199], yHat1.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "517.5711905386906"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yHat10 = lwlrTest(abX[100: 199], abX[0: 99], abY[0: 99], 10)\n",
    "reeError(abY[100: 199], yHat10.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "518.6363153250859"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws = standRegres(abX[0: 99], abY[0:99])\n",
    "yHat = mat(abX[100: 199]) * ws\n",
    "reeError(abY[100: 199], yHat.T.A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用于计算回归系数\n",
    "def ridgeRegres(xMat, yMat, lam=0.2):\n",
    "    xTx = xMat.T * xMat\n",
    "    denom = xTx + eye(shape(xMat)[1]) * lam\n",
    "    if linalg.det(denom) == 0.0:\n",
    "        print('This matrix is singular, cannot do inverse')\n",
    "        return\n",
    "    ws = denom.I * (xMat.T * yMat)\n",
    "    return ws\n",
    "\n",
    "def ridgeTest(xArr, yArr):\n",
    "    xMat = mat(xArr)\n",
    "    yMat = mat(yArr).T\n",
    "    \n",
    "    yMean = mean(yMat, 0)  # axis=0返回纵轴的平均值，axis=1返回横轴的平均值\n",
    "    yMat = yMat - yMean\n",
    "    \n",
    "    xMeans = mean(xMat, 0)\n",
    "    xVar = var(xMat, 0)  # 计算方差\n",
    "    xMat = (xMat - xMeans) / xVar\n",
    "    \n",
    "    numTestPts = 30  # 设置30个回归系数\n",
    "    wMat = zeros((numTestPts, shape(xMat)[1]))\n",
    "    for i in range(numTestPts):\n",
    "        ws = ridgeRegres(xMat, yMat, exp(i-10))\n",
    "        wMat[i, :] = ws.T\n",
    "    return wMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "abX, abY = loadDataSet('abalone.txt')\n",
    "ridgeWeights = ridgeTest(abX, abY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(ridgeWeights)\n",
    "plt.show()  # 横坐标是lambda值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 前向逐步线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对数据进行标准化处理\n",
    "def regularize(xMat): \n",
    "    inMat = xMat.copy()\n",
    "    inMeans = mean(inMat,0) \n",
    "    inVar = var(inMat,0)\n",
    "    inMat = (inMat-inMeans)/inVar\n",
    "    return inMat \n",
    "\n",
    "# 前向逐步线性回归\n",
    "# 参数：输入数据，预测变量，步长，迭代次数\n",
    "def stageWise(xArr, yArr, eps=0.01, numIt=100):\n",
    "    xMat = mat(xArr)\n",
    "    yMat = mat(yArr).T\n",
    "    yMean = mean(yMat, 0)\n",
    "    yMat = yMat - yMean\n",
    "    xMat = regularize(xMat)\n",
    "    \n",
    "    m, n = shape(xMat)\n",
    "    returnMat = zeros((numIt, n))\n",
    "    ws = zeros((n, 1))\n",
    "    wsTest = ws.copy()\n",
    "    wsMax = ws.copy()\n",
    "    for i in range(numIt):  # 迭代numIt次\n",
    "        print(ws.T)\n",
    "        lowestError = inf\n",
    "        for j in range(n):  # 遍历每条数据\n",
    "            for sign in [-1, 1]:\n",
    "                wsTest = ws.copy()\n",
    "                wsTest[j] += eps * sign\n",
    "                yTest = xMat * wsTest\n",
    "                rssE = reeError(yMat.A, yTest.A)\n",
    "                if rssE < lowestError:\n",
    "                    lowestError = rssE\n",
    "                    wsMax = wsTest\n",
    "        ws = wsMax.copy()\n",
    "        returnMat[i, :] = ws.T\n",
    "    return returnMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "[[ 0.05  0.    0.09  0.03  0.31 -0.64  0.    0.36]]\n",
      "[[ 0.04  0.    0.09  0.03  0.31 -0.64  0.    0.36]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       [ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       [ 0.  ,  0.  ,  0.  , ...,  0.  ,  0.  ,  0.  ],\n",
       "       ...,\n",
       "       [ 0.05,  0.  ,  0.09, ..., -0.64,  0.  ,  0.36],\n",
       "       [ 0.04,  0.  ,  0.09, ..., -0.64,  0.  ,  0.36],\n",
       "       [ 0.05,  0.  ,  0.09, ..., -0.64,  0.  ,  0.36]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xArr, yArr = loadDataSet('abalone.txt')\n",
    "stageWise(xArr, yArr, 0.01, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.043 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n",
      "[[ 0.044 -0.011  0.12   0.022  2.023 -0.963 -0.105  0.187]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.   ,  0.   ,  0.   , ...,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   , ...,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   , ...,  0.   ,  0.   ,  0.   ],\n",
       "       ...,\n",
       "       [ 0.043, -0.011,  0.12 , ..., -0.963, -0.105,  0.187],\n",
       "       [ 0.044, -0.011,  0.12 , ..., -0.963, -0.105,  0.187],\n",
       "       [ 0.043, -0.011,  0.12 , ..., -0.963, -0.105,  0.187]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stageWise(xArr, yArr, 0.001, 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 0.0430442 , -0.02274163,  0.13214087,  0.02075182,  2.22403814,\n",
       "         -0.99895312, -0.11725427,  0.16622915]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xMat = mat(xArr)\n",
    "yMat = mat(yArr).T\n",
    "xMat = regularize(xMat)\n",
    "yM = mean(yMat, 0)\n",
    "yMat = yMat - yM\n",
    "weights = standRegres(xMat, yMat.T)\n",
    "weights.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测乐高玩具套装的价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "商品 #4 没有出售\n",
      "商品 #11 没有出售\n",
      "商品 #4 没有出售\n",
      "商品 #16 没有出售\n",
      "商品 #18 没有出售\n",
      "商品 #21 没有出售\n",
      "商品 #22 没有出售\n",
      "商品 #2 没有出售\n",
      "商品 #6 没有出售\n",
      "商品 #7 没有出售\n",
      "商品 #9 没有出售\n",
      "商品 #16 没有出售\n",
      "商品 #5 没有出售\n",
      "商品 #6 没有出售\n",
      "商品 #10 没有出售\n",
      "商品 #12 没有出售\n"
     ]
    }
   ],
   "source": [
    "# 获取数据集\n",
    "# 来源：https://cuijiahua.com/blog/2017/12/ml_12_regression_2.html\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def scrapePage(retX, retY, inFile, yr, numPce, origPrc):\n",
    "    \"\"\"\n",
    "    函数说明:从页面读取数据，生成retX和retY列表\n",
    "    Parameters:\n",
    "        retX - 数据X\n",
    "        retY - 数据Y\n",
    "        inFile - HTML文件\n",
    "        yr - 年份\n",
    "        numPce - 乐高部件数目\n",
    "        origPrc - 原价\n",
    "    Returns:\n",
    "        无\n",
    "    Website:\n",
    "        https://www.cuijiahua.com/\n",
    "    Modify:\n",
    "        2017-12-03\n",
    "    \"\"\"\n",
    "    # 打开并读取HTML文件\n",
    "    with open(inFile, encoding='utf-8') as f:\n",
    "        html = f.read()\n",
    "    soup = BeautifulSoup(html)\n",
    "    i = 1\n",
    "    # 根据HTML页面结构进行解析\n",
    "    currentRow = soup.find_all('table', r = \"%d\" % i)\n",
    "    while(len(currentRow) != 0):\n",
    "        currentRow = soup.find_all('table', r = \"%d\" % i)\n",
    "        title = currentRow[0].find_all('a')[1].text\n",
    "        lwrTitle = title.lower()\n",
    "        # 查找是否有全新标签\n",
    "        if (lwrTitle.find('new') > -1) or (lwrTitle.find('nisb') > -1):\n",
    "            newFlag = 1.0\n",
    "        else:\n",
    "            newFlag = 0.0\n",
    "        # 查找是否已经标志出售，我们只收集已出售的数据\n",
    "        soldUnicde = currentRow[0].find_all('td')[3].find_all('span')\n",
    "        if len(soldUnicde) == 0:\n",
    "            print(\"商品 #%d 没有出售\" % i)\n",
    "        else:\n",
    "            # 解析页面获取当前价格\n",
    "            soldPrice = currentRow[0].find_all('td')[4]\n",
    "            priceStr = soldPrice.text\n",
    "            priceStr = priceStr.replace('$','')\n",
    "            priceStr = priceStr.replace(',','')\n",
    "            if len(soldPrice) > 1:\n",
    "                priceStr = priceStr.replace('Free shipping', '')\n",
    "            sellingPrice = float(priceStr)\n",
    "            # 去掉不完整的套装价格\n",
    "            if  sellingPrice > origPrc * 0.5:\n",
    "#                 print(\"%d\\t%d\\t%d\\t%f\\t%f\" % (yr, numPce, newFlag, origPrc, sellingPrice))\n",
    "                retX.append([yr, numPce, newFlag, origPrc])\n",
    "                retY.append(sellingPrice)\n",
    "        i += 1\n",
    "        currentRow = soup.find_all('table', r = \"%d\" % i)\n",
    "         \n",
    "def setDataCollect(retX, retY):\n",
    "    \"\"\"\n",
    "    函数说明:依次读取六种乐高套装的数据，并生成数据矩阵\n",
    "    Parameters:\n",
    "        无\n",
    "    Returns:\n",
    "        无\n",
    "    Website:\n",
    "        https://www.cuijiahua.com/\n",
    "    Modify:\n",
    "        2017-12-03\n",
    "    \"\"\"\n",
    "    scrapePage(retX, retY, './lego/lego8288.html', 2006, 800, 49.99)                #2006年的乐高8288,部件数目800,原价49.99\n",
    "    scrapePage(retX, retY, './lego/lego10030.html', 2002, 3096, 269.99)                #2002年的乐高10030,部件数目3096,原价269.99\n",
    "    scrapePage(retX, retY, './lego/lego10179.html', 2007, 5195, 499.99)                #2007年的乐高10179,部件数目5195,原价499.99\n",
    "    scrapePage(retX, retY, './lego/lego10181.html', 2007, 3428, 199.99)                #2007年的乐高10181,部件数目3428,原价199.99\n",
    "    scrapePage(retX, retY, './lego/lego10189.html', 2008, 5922, 299.99)                #2008年的乐高10189,部件数目5922,原价299.99\n",
    "    scrapePage(retX, retY, './lego/lego10196.html', 2009, 3263, 249.99)                #2009年的乐高10196,部件数目3263,原价249.99\n",
    "\n",
    "\n",
    "lgX = []\n",
    "lgY = []\n",
    "setDataCollect(lgX, lgY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[2006, 800, 0.0, 49.99],\n",
       "  [2006, 800, 0.0, 49.99],\n",
       "  [2006, 800, 0.0, 49.99],\n",
       "  [2006, 800, 0.0, 49.99],\n",
       "  [2002, 3096, 0.0, 269.99],\n",
       "  [2002, 3096, 0.0, 269.99],\n",
       "  [2002, 3096, 0.0, 269.99],\n",
       "  [2002, 3096, 0.0, 269.99],\n",
       "  [2002, 3096, 0.0, 269.99],\n",
       "  [2002, 3096, 1.0, 269.99],\n",
       "  [2002, 3096, 0.0, 269.99],\n",
       "  [2002, 3096, 1.0, 269.99],\n",
       "  [2002, 3096, 0.0, 269.99],\n",
       "  [2002, 3096, 1.0, 269.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 0.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 5195, 1.0, 499.99],\n",
       "  [2007, 3428, 0.0, 199.99],\n",
       "  [2007, 3428, 0.0, 199.99],\n",
       "  [2007, 3428, 0.0, 199.99],\n",
       "  [2007, 3428, 0.0, 199.99],\n",
       "  [2007, 3428, 1.0, 199.99],\n",
       "  [2007, 3428, 1.0, 199.99],\n",
       "  [2007, 3428, 0.0, 199.99],\n",
       "  [2008, 5922, 1.0, 299.99],\n",
       "  [2008, 5922, 1.0, 299.99],\n",
       "  [2008, 5922, 0.0, 299.99],\n",
       "  [2008, 5922, 0.0, 299.99],\n",
       "  [2008, 5922, 1.0, 299.99],\n",
       "  [2008, 5922, 1.0, 299.99],\n",
       "  [2008, 5922, 1.0, 299.99],\n",
       "  [2008, 5922, 1.0, 299.99],\n",
       "  [2008, 5922, 0.0, 299.99],\n",
       "  [2008, 5922, 1.0, 299.99],\n",
       "  [2008, 5922, 0.0, 299.99],\n",
       "  [2009, 3263, 1.0, 249.99],\n",
       "  [2009, 3263, 1.0, 249.99],\n",
       "  [2009, 3263, 1.0, 249.99],\n",
       "  [2009, 3263, 0.0, 249.99],\n",
       "  [2009, 3263, 1.0, 249.99],\n",
       "  [2009, 3263, 1.0, 249.99],\n",
       "  [2009, 3263, 1.0, 249.99],\n",
       "  [2009, 3263, 0.0, 249.99],\n",
       "  [2009, 3263, 1.0, 249.99]],\n",
       " [85.0,\n",
       "  102.5,\n",
       "  77.0,\n",
       "  162.5,\n",
       "  699.99,\n",
       "  602.0,\n",
       "  515.0,\n",
       "  510.0,\n",
       "  375.0,\n",
       "  1050.0,\n",
       "  740.0,\n",
       "  759.0,\n",
       "  730.0,\n",
       "  750.0,\n",
       "  910.0,\n",
       "  1199.99,\n",
       "  811.88,\n",
       "  1324.79,\n",
       "  850.0,\n",
       "  800.0,\n",
       "  810.0,\n",
       "  1075.0,\n",
       "  1050.0,\n",
       "  1199.99,\n",
       "  1342.31,\n",
       "  1000.0,\n",
       "  1780.0,\n",
       "  750.0,\n",
       "  2204.99,\n",
       "  925.0,\n",
       "  860.0,\n",
       "  1199.99,\n",
       "  1099.99,\n",
       "  1149.99,\n",
       "  800.0,\n",
       "  850.0,\n",
       "  469.95,\n",
       "  479.0,\n",
       "  299.99,\n",
       "  369.0,\n",
       "  424.95,\n",
       "  380.0,\n",
       "  305.0,\n",
       "  530.0,\n",
       "  599.95,\n",
       "  510.0,\n",
       "  423.0,\n",
       "  599.99,\n",
       "  589.99,\n",
       "  569.99,\n",
       "  529.99,\n",
       "  500.0,\n",
       "  549.95,\n",
       "  300.0,\n",
       "  380.0,\n",
       "  399.0,\n",
       "  427.99,\n",
       "  360.0,\n",
       "  399.0,\n",
       "  399.95,\n",
       "  499.99,\n",
       "  399.95,\n",
       "  331.51])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgX, lgY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 4)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(lgX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgX1 = mat(ones((63, 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([2006, 800, 0.0, 49.99],\n",
       " matrix([[1.000e+00, 2.006e+03, 8.000e+02, 0.000e+00, 4.999e+01]]))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgX1[:, 1:5] = mat(lgX)\n",
    "lgX[0], lgX1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 5.53199701e+04],\n",
       "        [-2.75928219e+01],\n",
       "        [-2.68392234e-02],\n",
       "        [-1.12208481e+01],\n",
       "        [ 2.57604055e+00]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ws = standRegres(lgX1, lgY)\n",
    "ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(matrix([[76.07418847]]), 85.0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgX1[0] * ws, lgY[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(matrix([[431.17797666]]), 331.51)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgX1[-1] * ws, lgY[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(matrix([[516.20733099]]), 530.0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgX1[43] * ws, lgY[43]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 交叉验证测试岭回归\n",
    "def crossValidation(xArr, yArr, numVal=10):\n",
    "    m = len(yArr)\n",
    "    indexList = list(range(m))\n",
    "    errorMat = zeros((numVal, 30))\n",
    "    for i in range(numVal):\n",
    "        trainX = []\n",
    "        trainY = []\n",
    "        testX = []\n",
    "        testY = []\n",
    "        random.shuffle(indexList)\n",
    "        for j in range(m):\n",
    "            if j < m * 0.9:\n",
    "                trainX.append(xArr[indexList[j]])\n",
    "                trainY.append(yArr[indexList[j]])\n",
    "            else:\n",
    "                testX.append(xArr[indexList[j]])\n",
    "                testY.append(yArr[indexList[j]])\n",
    "                \n",
    "        wMat = ridgeTest(trainX, trainY)\n",
    "        for k in range(30):\n",
    "            matTestX = mat(testX)\n",
    "            matTrainX = mat(trainX)\n",
    "            meanTrain = mean(matTrainX, 0)\n",
    "            varTrain = var(matTrainX, 0)\n",
    "            matTestX = (matTestX - meanTrain) / varTrain\n",
    "            yEst = matTestX * mat(wMat[k, :]).T + mean(trainY)\n",
    "            errorMat[i, k] = reeError(yEst.T.A, array(testY))\n",
    "    meanErrors = mean(errorMat, 0)\n",
    "    minMean = float(min(meanErrors))\n",
    "    bestWeights = wMat[nonzero(meanErrors == minMean)]\n",
    "    xMat = mat(xArr)\n",
    "    yMat = mat(yArr).T\n",
    "    meanX = mean(xMat, 0)\n",
    "    varX = var(xMat, 0)\n",
    "    unReg = bestWeights / varX\n",
    "    print('the best mode1 from Ridge Regression is: \\n', unReg)\n",
    "    print('with constant term: ', -1 * sum(multiply(meanX, unReg)) + mean(yMat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the best mode1 from Ridge Regression is: \n",
      " [[-3.17737202e+01 -2.86905354e-03  3.17099911e+01  2.26220223e+00]]\n",
      "with constant term:  63690.5407401018\n"
     ]
    }
   ],
   "source": [
    "crossValidation(lgX, lgY, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.45288906e+02, -8.39360442e+03, -3.28682450e+00,\n",
       "         4.42362406e+04],\n",
       "       [-1.46649725e+02, -1.89952152e+03, -2.80638599e+00,\n",
       "         4.27891633e+04],\n",
       "       [-1.44450432e+02,  8.55488076e+02, -1.35089285e+00,\n",
       "         4.00885735e+04],\n",
       "       [-1.37402474e+02,  1.64217093e+03,  1.95840783e+00,\n",
       "         3.44932120e+04],\n",
       "       [-1.24750588e+02,  1.44326171e+03,  7.62540167e+00,\n",
       "         2.50647592e+04],\n",
       "       [-1.10234679e+02,  8.81842164e+02,  1.40617304e+01,\n",
       "         1.43874420e+04],\n",
       "       [-9.96484167e+01,  4.17805568e+02,  1.87140361e+01,\n",
       "         6.66770425e+03],\n",
       "       [-9.40345090e+01,  1.71289137e+02,  2.10844952e+01,\n",
       "         2.71206176e+03],\n",
       "       [-9.11400659e+01,  6.57287394e+01,  2.20487105e+01,\n",
       "         1.03800465e+03],\n",
       "       [-8.86246985e+01,  2.45452725e+01,  2.23181664e+01,\n",
       "         3.87564774e+02],\n",
       "       [-8.41447674e+01,  9.05861459e+00,  2.21495534e+01,\n",
       "         1.43313895e+02],\n",
       "       [-7.44804291e+01,  3.31863501e+00,  2.14607512e+01,\n",
       "         5.27810178e+01],\n",
       "       [-5.68008473e+01,  1.20770663e+00,  2.00168153e+01,\n",
       "         1.93999701e+01],\n",
       "       [-3.43546503e+01,  4.38238026e-01,  1.77836684e+01,\n",
       "         7.12719906e+00],\n",
       "       [-1.62951276e+01,  1.59882766e-01,  1.48514658e+01,\n",
       "         2.62234165e+00],\n",
       "       [-6.48291858e+00,  5.89025383e-02,  1.09847950e+01,\n",
       "         9.67404902e-01],\n",
       "       [-2.35268585e+00,  2.18391027e-02,  6.61152257e+00,\n",
       "         3.57478187e-01],\n",
       "       [-8.35001919e-01,  8.09519290e-03,  3.19552087e+00,\n",
       "         1.32007993e-01],\n",
       "       [-2.99711902e-01,  2.99108326e-03,  1.33043325e+00,\n",
       "         4.86659524e-02],\n",
       "       [-1.08982743e-01,  1.10249682e-03,  5.14449554e-01,\n",
       "         1.79199150e-02],\n",
       "       [-3.99038878e-02,  4.05898142e-04,  1.92885670e-01,\n",
       "         6.59479857e-03],\n",
       "       [-1.46534283e-02,  1.49365062e-04,  7.14631760e-02,\n",
       "         2.42642878e-03],\n",
       "       [-5.38707897e-03,  5.49542829e-05,  2.63587872e-02,\n",
       "         8.92679468e-04],\n",
       "       [-1.98130399e-03,  2.02173589e-05,  9.70622185e-03,\n",
       "         3.28404700e-04],\n",
       "       [-7.28814363e-04,  7.43766021e-06,  3.57198872e-03,\n",
       "         1.20814188e-04],\n",
       "       [-2.68106796e-04,  2.73617711e-06,  1.31423307e-03,\n",
       "         4.44451712e-05],\n",
       "       [-9.86297565e-05,  1.00658531e-06,  4.83502591e-04,\n",
       "         1.63504803e-05],\n",
       "       [-3.62836944e-05,  3.70302314e-07,  1.77873811e-04,\n",
       "         6.01500768e-06],\n",
       "       [-1.33480028e-05,  1.36226645e-07,  6.54365445e-05,\n",
       "         2.21279795e-06],\n",
       "       [-4.91045279e-06,  5.01149871e-08,  2.40728171e-05,\n",
       "         8.14042912e-07]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgeTest(lgX, lgY)  # 查看回归系数变化情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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